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Richard Valliant, Ph.D., Frauke Kreuter, Ph.D., Mariel Leonard, Frederick Conrad, Ph.D., and James M Lepkowski

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data.

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This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data.

Faculty for this specialisation comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection firm Westat, founded by the National Science Foundation and the Interagency Consortium of Statistical Policy in the U.S. to educate the next generation of survey researchers, survey statisticians, and survey methodologists. In addition to this specialization we offer short courses, a summer school, certificates, master degrees as well as PhD programs.

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What's inside

Seven courses

Framework for Data Collection and Analysis

(0 hours)
This course provides an overview of data products and the data collection landscape. It teaches how to identify data sources, turn research questions into measurable pieces, and create an analysis plan. The course also introduces a framework for understanding and evaluating data collection and analysis steps and errors. Finally, it reviews large-scale data collection efforts by industry and government agencies.

Data Collection: Online, Telephone and Face-to-face

(0 hours)
This course examines research on how data collection decisions affect survey errors. It reviews literature on survey design decisions and data quality to sensitize learners to how alternative survey designs might impact the data obtained from those surveys.

Questionnaire Design for Social Surveys

(0 hours)
This course covers the basics of designing and evaluating questionnaires. We will review the process of responding to questions, challenges, and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode-specific questionnaire characteristics, and review methods of standardized and conversational interviewing.

Sampling People, Networks and Records

Good data collection is built on good samples. This course examines simple random sampling, cluster sampling, stratification, systematic selection, and stratified multistage samples. It concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.

Dealing With Missing Data

(0 hours)
This course covers weighting sample surveys, adjusting for nonresponse, and using external data for calibration. Techniques include response propensities, poststratification, raking, and regression estimation. Imputation methods for missing items are also discussed. Statistical software packages like R, Stata, and SAS are covered.

Combining and Analyzing Complex Data

In this course, you will learn how to use survey weights to estimate descriptive statistics and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis.

Survey Data Collection and Analytics Project (Capstone)

(0 hours)
The Capstone Project enables learners to apply their knowledge by analyzing and comparing multiple data sources on the same topic. Students will develop a research question, access and analyze relevant data, and critically examine the quality of each data source.

Learning objectives

  • Understand the basics of survey design, sampling methods, data collection and data analysis
  • Apply the skills learned to analyze and compare multiple data sources in a capstone project

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